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NATIONAL TECHNICAL UNIVERSITY OF ATHENS
Electrical and Computer Engineering School
Department of Computer Science
Image, Video and Multimedia Laboratory
Organizing research publications in
Web 3 enviroment
Anastasiou Lucas [email protected]
Vasilis Tzouvaras [email protected]
Stefanos Kollias
Application description
Online reference software
Social bookmarking (e.g. delicious.com)
Semantic content social networking services
Use as collaboration tool
Reasoning services
Quick and sophisticated queries
Web 1.0
⇒ Web 2.0
Tranformation of web applications
Encarta
→
Personal websites
→
Content management systems
doubleClick
→
Directories(taxonomy)
→
→
→
→
→
→
wikipedia
blogs
→ wikis
AdSense
tagging (folksonomies)
syndication (RSS)
user generated content
RIAs
social networks
...
facilitates collaboration, communication, information
sharing and interoperability
Folksonomy
Social tagging
Classification and annotation of
content
«Wisdom of the crowds»
What Web 2.0 is not
Not a new version of internet but a new
approach using existing technologies
“Web 1.0 was all about connecting people. It
was an interactive space, and I think Web 2.0 is,
of course, a piece of jargon, nobody even knows
what it means. If Web 2.0 for you is blogs and
wikis, then that is people to people. But that
was what the Web was supposed to be all
along.”
Tim Berners Lee
Web 2.0 is not Semantic Web
Semantic Web
Today’s web suitable only for human consumption
rather than using by machines or web agents
Web 2.0 gives the social aspect of web while semantic
web pursues to:
Get all information machine-readable. Information
should be machine readable and processable by
machines-web agents.
Organize knowledge in conceptual spaces
according to its meaning
Use knowledge representation techniques to get
implicit knowledge (automated reasoning) from
pages content.
Description Logics
Knowledge representation languages
Strict and formal mathematical way to describe a domain, yet
simple and elegant
Mathematical background of OWL
Descibes a knowledge domain using atomic concepts C,
atomic roles R and individuals I
Constructors {¬, ⊓, ⊔, ∀, ∃, ≥n, ≤n, {}, ...} to build complex
concepts
Terminological axioms
Description Logics - tradeoff
DLs with efficient reasoning algorithms lack modeling
power
DLs with high modelling power suffer of high complexity
DL-Lite
Low expressivity capable though to capture conceptual
modeling formalisms used in databases and software
engineering such as ER and UML class diagrams
Queries run in polynomial time with respect to the size of
ABox of Knowledge Base
⇒ tractable
Ideal as conceptual layer to store instances to traditional
relational databases
⇒ persistance
Fuzzy-DL-Lite
The fuzzy extension of DL-Lite
Fuzzy assertions
〈 (George,Artificial Intelligence) : isInterestedIn ≥0.9 〉
〈 George : Tall ≥0.7 〉
Fuzzy-DL-Lite reasoner
Works great with traditional relational databases (Oracle,
mySQL, SQLite, …)
Checks consistency of knowledge
Features:
Threshold query
Top-k query
Ontology
Adding a new publication
Entering the following bibTex
@article{horrocks2003sar,
title={{From SHIQ and RDF to OWL: The making of a web ontology
language}},
author={Horrocks, I. and Patel-Schneider, P.F. and Van Harmelen, F.},
journal={Web semantics: science, services and agents on the World Wide
Web},
year={2003},
publisher={Elsevier}
}
results to creation of following assertions:
Application
Use of inference engine for reasoning
Quick queries through the conceptual
layer
Semantic annotation of publications
User generated metadata
Conjunctive Queries
Get all publications which belong on area of
Artificial Intelligence by 50%:
q(x) ← Publication(x):1.0 ∧ isOnTheArea(x,y):0.5 ∧
Artificial_Intelligence(y):1.0
Get all authors who are interested in Computer
Graphics by 50%
q(x) ← Authors(x):1.0 ∧ isInterestedIn(x,y):0.5 ∧
Computer_Graphics(y):1.0
Contact and links
[email protected]
[email protected]
http://delicious.com/anastluc/EIE2009
http://users.ntua.gr/el03605/presentation.ppt
http://localhost:8084/ImageLabv2/home.jsp